Have you ever thought about how websites like Facebook, Wikipedia, Amazon, Flipkart, and YouTube manage their large user data? How they make decisions for their marketing campaigns? What kind of technology supports large data?
In this era of evolving technology, database management systems (DBMS) are no longer limited to their traditional functions of managing data. With the advent of disruptive technologies such as Digital Marketing, Internet of Things, and Artificial Intelligence (AI), the complexity and proliferation of data are on a sharp rise.
What is Database Management System (DBMS)?
Database Management Systems (DBMS) is a technology for creating and managing a database. DBMS is a software tool which is used to organize (create, retrieve, update and manage) data in a database. The main aim of DBMS is to provide a convenient and efficient way to store and retrieve database information. Moreover, the database system must ensure the safety of the information stored, despite system crashes or attempts at unauthorized access.
MySQL is one of the most famous DBMS. MySQL is a relational database management system (RDBMS), which I will discuss later. Some famous websites like Facebook, Amazon, and Flipkart use MySQL for their data management. Developers use it in web applications as well. RDBMS supports standard SQL (Structured Query Language).
What is SQL?
SQL stands for Structured Query Language. SQL definition says that it is a standard database language which is used to Create, Manipulate and Retrieve database, especially relational database. Relational database means the data is stored as well retrieved in the form of relations (tables). Suppose a table contains information about students such as Roll Number, Name, Contact Number and Class. This Student Table is a relational database with only one relation called Student.
SQL became a standard of the American National Standards Institute (ANSI) and International Organization for Standardization (ISO) for relational database management systems (RDBMS). RDBMS is the basis for SQL, and for all other database systems such as MS SQL Server, Oracle, MySQL, and Microsoft Access. Mostly SQL is useful in handling structured data where there are relations between different entities or variables of the data. There are different versions of SQL language, they all support the major commands of SQL such as SELECT UPDATE, DELETE, INSERT and WHERE.
How we can use SQL?
With SQL you can execute queries against a large database system to extract a piece of information. Operations like INSERT, UPDATE and DELETE records from database are easy to perform. You can also add new tables and create a new database. SQL is great for running queries on big tables. It can handle million-line data sets efficiently.
Big data and cloud computing are now mainstream jargons in the IT sector. From accurate sensors to automation, data now needs to be handled in real-time. This is where SQL, the programming language used to maintain DBMS. The development in DBMS evolved other functions such as data transformation, security, and other useful features.
SQL in Digital Marketing
SQL plays an important role in the field of Digital Marketing, especially in E-commerce. As data is an important requirement to run effective digital marketing campaigns. SQL helps digital marketing analysts or web analysts to understand user data. One of the most common analysis in E-commerce is who are our top customers, who are our worst customers. Digital Marketing Analysts can perform this analysis very easily using SQL queries, without taking any help from IT people. SQL is also used in accessing in-depth user data from your company’s database, running advanced queries on Google Analytics data, etc.
Digital Analysts should also know that BigQuery, a Google product, lets you use SQL to manage Google Analytics data. For example, you can join different Google Analytics tables to create reports that are not possible in the Google Analytics interface. The standard Google Analytics interface lets you use primary and secondary dimensions to create segments by, for example, looking at a number of sessions by source/medium and default channel grouping. But, if you know SQL, you can segment your data further by adding a third and fourth dimension (e.g. location and device type). You can also add advanced filters (count only top 10% most-active users) and many other things.
SQL is quite easy to learn as compared to other languages. You can easily reach an intermediate level in SQL just in one week. There are many sources available on the internet like w3schools, geeksforgeeks, etc.